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Technical University of Munich
Technical University of Munich

Organizational and Power Dynamics in Data Management and Governance

Advisor: Philipp Kernstock

CONTEXT

Are you passionate about understanding the intricate dynamics of organizations and the transformative power of data? We invite you to embark on an exciting journey with us, focusing on the evolving data management and governance landscape.

Organizations are transitioning towards more decentralized data management structures in today's rapidly democratizing data environment (Lefebvre et al., 2023). This shift is accompanied by newly emerging data management paradigms such as data products and data mesh (Blohm et al., 2024). These approaches necessitate the creation of data domains, each responsible for managing its own data (Dehghani, 2019). However, establishing these domains within an existing organizational framework presents complex challenges that are deeply rooted in organizational and power dynamics (Benfeldt et al., 2020; Santos & Eisenhardt, 2005).

OBJECTIVES

Your thesis will explore:

  1. Literature Review: Conduct a comprehensive review of existing literature on organizational and power dynamics, mapping these insights to the realm of data governance.
  2. Multiple-Case Study: Investigate how various organizations navigate the domain creation and splitting process, analyzing real-world examples to understand best practices and common pitfalls. Develop these practical insights into theoretical contributions.

WHY JOIN US?

  • Cutting-edge Research: Contribute to a critical area of study that blends organizational theory with practical data governance challenges.
  • Expert Supervision: Benefit from personalized guidance and mentorship from experienced faculty.
  • Real-world Impact: Your findings will help shape how organizations approach data management, influencing industry practices and standards.

IDEAL CANDIDATE

  • Background in business administration, information systems, or related fields.
  • Strong interest in organizational behavior, power dynamics, and data governance.
  • Excellent analytical and research skills.

APPLICATION

Interested candidates are encouraged to apply by 30th July. Please submit your CV, a brief statement of interest, and any relevant academic work to philipp.kernstock@tum.de.

Take this opportunity to delve into a dynamic field that sits at the intersection of organizational theory and data science. Join us in exploring how organizations can effectively manage data in an era of democratization.

 

REFERENCES

Benfeldt, O., Persson, J. S., & Madsen, S. (2020). Data Governance as a Collective Action Problem. Information Systems Frontiers, 22(2), 299-313.

Blohm, I., Wortmann, F., Legner, C., & Köbler, F. (2024). Data products, data mesh, and data fabric: New paradigm (s) for data and analytics? Business & Information Systems Engineering, 1-10.

Dehghani, Z. (2019). How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh. Retrieved 2023-09-21 from https://martinfowler.com/articles/data-monolith-to-mesh.html

Lefebvre, H., Legner, C., & Teracino, E. (2023). 5 Pillars for Democratizing Data at Your Organization. Harvard Business Review. https://hbr.org/2023/11/5-pillars-for-democratizing-data-at-your-organization

Santos, F. M., & Eisenhardt, K. M. (2005). Organizational boundaries and theories of organization. Organization Science, 16(5), 491-508.

 

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